Publication: An Improved Hierarchical Deep Reinforcement Learning For Complex Imperfect-Information Card Games
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Date
2025-03
Authors
Luo, Qian
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Abstract
Deep reinforcement learning (drl) has achieved significant breakthroughs in a variety of games, both with perfect and imperfect information, such as go, texas hold’em, and starcraft ii. However, doudizhu and big2 are classic complex card games with imperfect information and are popular in asia. They present new challenges for ai in competition, cooperation, inferring imperfect information, handling large state-action spaces, and training with sparse rewards. The deep monte carlo (dmc) method for these card games achieves significant success but still faces three key research problems: slowlearning speed, high loss during learning, and performance optimization. The primary objective of this research is to enhance the performance for these complex imperfect-information card games with a hierarchical deep reinforcement learning (hdrl) framework. Specifically, this main goal is divided into three sub-research objectives: improving learning efficiency indmctraining through oracle guiding, enhancing learning stability with adaptive deep monte carlo (admc), and improving the performance of proximal policy optimization (ppo) using relative advantage reward shaping (rars).
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Reinforcement learning